Analyzing and Visualizing BHSC using scVAT

Initializing VATEntity object

scVAT can build VATEntity object from Single-Cell RNA-seq data from a csv, sparse mtx or h5 file. Firstly, loading scVAT package

library(scVAT)
#> Warning: replacing previous import 'igraph::groups' by 'plotly::groups'
#> when loading 'scVAT'
#> Warning: replacing previous import 'shiny::dataTableOutput' by
#> 'DT::dataTableOutput' when loading 'scVAT'
#> Warning: replacing previous import 'shiny::renderDataTable' by
#> 'DT::renderDataTable' when loading 'scVAT'

Loading raw data from CSV File, then initializing VATEntity object

#loading expression matrix from CSV, The csv file should contain genes in the row and cells in the columns. 
BHSC.data <- loadCSVData("BHSC.csv")
#initializing VATEntity object using loaded data
BHSC <- initVATEntity(BHSC.data, title="BHSC")
#> 2018-03-28 16:15:46 create VAT Entity... 
#> 2018-03-28 16:15:49 Filtering cells and genes... 
#> 2018-03-28 16:15:49 Scaling and Normalizing data... 
#> 2018-03-28 16:15:53 It's done!
#print BHSC
BHSC
#> An object of  VATEntity in dataset' BHSC '
#>  15616 genes,  2143 cells.

Doing analysis

Running the base pipline, including PCA, t-SNE, and Cluster

#do PCA, pc.num = 50
BHSC <- doPCA(BHSC, pc.num = 50)
#Plot PC standard deviation
plotPCASDev(BHSC)
#> Warning: package 'bindrcpp' was built under R version 3.4.3

#do tSNE, using PC1~50
BHSC <- doTSNE(BHSC, dims = 2, use.col = 50)

Loading external analysis results

scVAT can integrate the third party anlaysis results, and visualizing them. For the demo, Loading PHATE result(via)

#specifying data file, dimensions and key value (important, the key will be used later)
BHSC <- loadAnalysisFromCSV(BHSC, "BHSC_PHATE.csv", ndims =3, key="PHATE")

Visualizing one gene expression

#plot one gene expression based on t-SNE
plotGene(BHSC, genes="Cdc20", dims = c(1,2))

#plot one gene expression based on t-SNE, no gradient, and different colors
plotGene(BHSC, genes="Cdc20", gradient= FALSE, dims = c(1,2), colors=c("grey","red"))

#plot one gene expression based on PHATE
plotGene(BHSC, genes="Cdc20", dims = c(1,2), key="PHATE")

#plot one gene expression based on PHATE (3D),parameters size and sizes set point size for 3D maps
plotGene(BHSC, genes="Cdc20", dims = c(1,2,3), key="PHATE",size=1,sizes=c(1,5))

Visualizing two or three gene expressions

#plot two genes based on tSNE
plotTwoGenes(BHSC, gene1 = "Cdc20",gene2 = "Gata1")

#plot two genes based on tSNE, and use different colors
plotTwoGenes(BHSC, gene1 = "Cdc20",gene2 = "Gata1",colors=c("grey","blue","red","black"))

#plot two genes based on PHATE, and use different colors
plotTwoGenes(BHSC, gene1 = "Cdc20", gene2 = "Gata1", dims=c(1,2,3),key = "PHATE", size=1, sizes=c(1,5))

#plot three genes based on PHATE
plotThreeGenes(BHSC, gene1 = "Cdc20", gene2 = "Gata1", gene3 = "Klf1", dims=c(1:3),key="PHATE", size =1, sizes = c(1,5))

Visualizing many gene expressions in one map at the sametime

#Plot many genes at the sametime, nrows sets row number
plotGenes(BHSC, genes = c("Cdc20","Gata1","Klf1","Ube2c"), nrows=2)

Start Web GUI

#start Web GUI for visualization, clustering manually, and differential analysis
startVATGUI("BHSC")
#> Loading required package: shiny
#> Warning: package 'shiny' was built under R version 3.4.3
#> Loading required package: shinydashboard
#> Warning: package 'shinydashboard' was built under R version 3.4.3
#> 
#> Attaching package: 'shinydashboard'
#> The following object is masked from 'package:graphics':
#> 
#>     box
#> Loading required package: plotly
#> Warning: package 'plotly' was built under R version 3.4.3
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 3.4.3
#> 
#> Attaching package: 'plotly'
#> The following object is masked from 'package:ggplot2':
#> 
#>     last_plot
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following object is masked from 'package:graphics':
#> 
#>     layout
#> 
#> Listening on http://127.0.0.1:7735

The html_vignette template includes a basic CSS theme. To override this theme you can specify your own CSS in the document metadata as follows:

output: 
  rmarkdown::html_vignette:
    css: mystyles.css

Figures

The figure sizes have been customised so that you can easily put two images side-by-side.

plot(1:10)
plot(10:1)

You can enable figure captions by fig_caption: yes in YAML:

output:
  rmarkdown::html_vignette:
    fig_caption: yes

Then you can use the chunk option fig.cap = "Your figure caption." in knitr.

More Examples

You can write math expressions, e.g. \(Y = X\beta + \epsilon\), footnotes¹, and tables, e.g. using knitr::kable().

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2
Valiant	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1
Duster 360	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4
Merc 240D	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2
Merc 230	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2
Merc 280	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4

Also a quote using >:

“He who gives up [code] safety for [code] speed deserves neither.” (via)